Many expression studies have so far focused on devising methods to cluster genes by similarities in expression profiles. This is in order to determine the proteins that are expressed together under different cellular conditions. Briefly, the most common methods are hierarchical clustering, self-organising maps, and K-means clustering. Hierarchical methods originally derived from algorithms to construct phylogenetic trees, and group genes in a “ bottom up” fashion; genes with the most similar expression profiles are clustered first, and those with more diverse profiles are included iteratively. In contrast, the self- rganising map and K-means methods employ a “top-down” approach in which the user pre-defines the number of clusters for the dataset. The clusters are initially assigned randomly, and the genes are regrouped iteratively until they are optimally clustered.

Given these methods, it is of interest to relate the expression data to other attributes such as structure, function and sub cellular localisation of each gene product. Mapping these properties provides an insight into the characteristics of proteins that are expressed together, and also suggest some interesting conclusions about the overall biochemistry of the cell. In yeast, shorter proteins tend to be more highly expressed than longer proteins, probably because of the relative ease with which they are produced. Looking at the amino acid content, highly expressed genes are generally enriched in alanine and glycine, and depleted in asparagine; these are thought to reflect the requirements of amino acid usage in the organism, where synthesis of alanine and glycine are energetically less expensive than asparagine. Turning to protein structure, expression levels of the TIM barrel and NTP hydrolase folds are highest, while those for the leucine zipper, zinc finger and trans-membrane helix-containing folds are lowest. This relates to the functions associated with these folds; the former are commonly involved in metabolic pathways and the latter in signalling or transport processes. This is also reflected in the relationship with sub-cellular localisations of proteins, where expression of cytoplasmic proteins is high, but nuclear and membrane proteins tend to be low.

More complex relationships have also been assessed. Conventional wisdom is that gene products that interact with each other are more likely to have similar expression profiles than if they do not. However, are cent study showed that this relationship is not so simple. While expression profiles are similar for gene products that are permanently associated, for example in the large ribosomal subunit, profiles differ significantly for products that are only associated transiently, including those belonging to the same metabolic pathway.

As described below, one of the main driving forces behind expression analysis has been to analyse cancerous cell lines. In general, it has been shown that different cell lines (eg epithelial and ovarian cells) can be distinguished on the basis of their expression profiles, and that these profiles are maintained when cells are transferred from an in vivo to an in vitro environment. The basis for their physiological differences were apparent in the expression of specific genes; for example, expression levels of gene products necessary for progression through the cell cycle, especially ribosomal genes, correlated well with variations in cell proliferation rate. Comparative analysis can be extended to tumour cells, in which the underlying causes of cancer can be uncovered by pinpointing areas of biological variations compared to normal cells. For example in breast cancer, genes related to cell proliferation and the IFN-regulated signal transduction pathway were found to be up regulated. One of the difficulties in cancer treatment has been to target specific therapies to pathogenetically distinct tumour types, in order to maximise efficacy and minimize toxicity. Thus, improvements in cancer classifications have been central to advances in cancer treatment. Although the distinction between different forms of cancer – for example subclasses of acute leukaemia – has been well established, it is still not possible to establish a clinical diagnosis on the basis of a single test. In a recent study, acute myeloid leukaemia and acute lymphoblastic leukaemia were successfully distinguished based on the expression profiles of these cells. As the approach does not require prior biological knowledge of the diseases, it may provide a generic strategy for classifying all types of cancer.

Clearly, an essential aspect of understanding expression data lies in understanding the basis of transcription regulation. However, analysis in this area is still limited to preliminary analyses of expression levels in yeast mutants lacking key components of the transcription initiation complex.

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